



'''simple docstring'''



import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    '''kwargs, expected'''       ,		[
        ({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
        ({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10					)]),
        ({'''num_shards''': 10, '''max_num_jobs''': 10}, [range(A       ,		i + 1					) for i in range(10					)]),
        ({'''num_shards''': 1, '''max_num_jobs''': 10}, [range(1					)]),
        ({'''num_shards''': 10, '''max_num_jobs''': 3}, [range(0       ,		4					), range(4       ,		7					), range(7       ,		10					)]),
        ({'''num_shards''': 3, '''max_num_jobs''': 10}, [range(0       ,		1					), range(1       ,		2					), range(2       ,		3					)]),
    ]       ,		)
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Optional[int]					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      _distribute_shards(**A					)
							assert out == expected
@pytest.mark.parametrize(
    '''gen_kwargs, max_num_jobs, expected'''       ,		[
        ({'''foo''': 0}, 10, [{'''foo''': 0}]),
        ({'''shards''': [0, 1, 2, 3]}, 1, [{'''shards''': [0, 1, 2, 3]}]),
        ({'''shards''': [0, 1, 2, 3]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}, {'''shards''': [2]}, {'''shards''': [3]}]),
        ({'''shards''': [0, 1]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}]),
        ({'''shards''': [0, 1, 2, 3]}, 2, [{'''shards''': [0, 1]}, {'''shards''': [2, 3]}]),
    ]       ,		)
def        UpperCAmelCase (    A	:					Any       ,		A	:					int       ,		A	:					Optional[int]					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      _split_gen_kwargs(A       ,		A					)
							assert out == expected






@pytest.mark.parametrize(
    '''gen_kwargs, expected'''       ,		[
        ({'''foo''': 0}, 1),
        ({'''shards''': [0]}, 1),
        ({'''shards''': [0, 1, 2, 3]}, 4),
        ({'''shards''': [0, 1, 2, 3], '''foo''': 0}, 4),
        ({'''shards''': [0, 1, 2, 3], '''other''': (0, 1)}, 4),
        ({'''shards''': [0, 1, 2, 3], '''shards2''': [0, 1]}, RuntimeError),
    ]       ,		)
def        UpperCAmelCase (    A	:					List[str]       ,		A	:					Union[str, Any]					):
							if expected is RuntimeError:
														with pytest.raises(A					):
																					_number_of_shards_in_gen_kwargs(A					)
							else:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      _number_of_shards_in_gen_kwargs(A					)
														assert out == expected









'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					Optional[int]       ,		A	:					int					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   str         =      TaConfig.from_json_file(A					)
							print(F"""Building PyTorch model from configuration: {config}"""					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TaForConditionalGeneration(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(A       ,		A       ,		A					)

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Union[str, Any]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Tuple             =    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




